Published January 2021
| Submitted
Journal Article
Open
Trading Votes for Votes: A Laboratory Study
- Creators
- Casella, Alessandra
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Palfrey, Thomas R.
Chicago
An error occurred while generating the citation.
Abstract
Vote trading is ubiquitous in committees and legislatures, and yet we know very little about its properties. We explore this subject with a laboratory experiment. We propose a model of vote trading in which pairs of voters exchange votes whenever doing so is mutually advantageous. The resulting trading dynamics always converge to stable vote allocations–allocations where no further improving trades exist. The data show that stability has predictive power: vote allocations in the lab converge towards stable allocations, and individual vote holdings at the end of trading are in line with theoretical predictions. There is less support for the finer details of the trade-by-trade dynamics.
Additional Information
© 2020 Elsevier Inc. Received 26 April 2019, Available online 1 November 2020. We thank Kirill Pogorelskiy, Manuel Puente, Enrico Zanardo, and Krzysztof Zaremba for research assistance. We thank Micael Castanheira, Parkash Chander, Andrew Gelman, Michel LeBreton, Cesar Martinelli, Debraj Ray, Richard Van Weelden, Rajiv Vohra, and Alistair Wilson for detailed comments and suggestions. We are grateful to the editor, the advisory editor, and three referees for helpful comments and suggestions. The National Science Foundation (SES-1426560, SES-0617934) and The Gordon and Betty Moore Foundation (SES-1158) provided financial support. Part of the research was conducted while Casella was a Straus Fellow at NYU Law School and Palfrey was a Visiting Scholar at the Russell Sage Foundation. The hospitality and financial support of both institutions are gratefully acknowledged. An earlier version of this paper was part of a working paper entitled "Trading Votes for Votes: A Decentralized Matching Algorithm".Attached Files
Submitted - ExVoteTrading_Feb28_2020.pdf
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Additional details
- Eprint ID
- 106434
- DOI
- 10.1016/j.geb.2020.10.004
- Resolver ID
- CaltechAUTHORS:20201104-143445798
- NSF
- SES-1426560
- NSF
- SES-0617934
- Gordon and Betty Moore Foundation
- SES-1158
- New York University (NYU)
- Russell Sage Foundation
- Created
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2020-11-05Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field